What Is Natural Language Processing (NLP)?

As long foreseen in science fiction, we humans are growing comfortable talking to our computers. Today’s large language models, or LLMs for short, and AI agents are a big leap in that direction—and both owe their elocution skills to the field of natural language processing, or NLP. Every time you dictate a message to your phone, type in a rambling search question, or ask AI to summarize a document, NLP techniques and technologies kick in. They make sense of your utterances and generate responses in the kind of language you’d use to chat with your neighbor across the fence. That’s a development worth exploring.

What Is NLP?

NLP is a branch of artificial intelligence that enables computers to comprehend, generate, and manipulate human language. NLP applies to both speech and written text and can be used with all human languages. Some technologies and methods for NLP that have been around for decades have recently seen significant improvements, and in the last few years, popular LLMs, which depend on NLP techniques, have brought it into wider use. And the incorporation of LLMs into more complex work processes—in the form of AI agents—is set to increase the use of NLP in everyday life.

Definition and Overview

Today’s LLMs arise out of the scientific field of computational linguistics, or CL, which studies the computational modeling of human language, while NLP is the engineering discipline concerned with building computational methods that help computers understand, generate, and manipulate human language. Major breakthroughs of the past decade have been powered by machine learning, a branch of AI that develops systems that learn by example. Developments of the past few years have enabled machine learning to understand very complex patterns in large data sets, making it ideally suited to learning the intricacies of language.

Developers incorporating NLP into applications take advantage of two primary branches of NLP, one focused on understanding language and the other on generating new responses to queries. Natural language understanding, or NLU, is for tasks such as sentiment analysis, entity recognition, and key-phrase extraction. These tasks require NLP applications to parse text or speech to understand what’s being said, while natural language generation, or NLG, generates answers, translations, and summarizations based on understanding the sentiments and details in human language given to it. The growing number of LLMs available from cloud vendors or from open source sites, such as Hugging Face, incorporate both NLU and NLG in their operations.

Why Is NLP Important?

Ever-improving LLMs have transformed more rudimentary NLP, which could detect the meaning of a question and apply the proper canned answer, into a flexible interlocutor trained on petabytes of general-purpose data in sophisticated neural networks. As a result, computers can now understand the structure and meaning of human languages, allowing developers and application users to carry on more nuanced conversations with them. This has implications in business, analytics, human relations, customer service, healthcare, and more—as data and documents become easily searched and summarized, they’re more valuable than ever. Below are some examples of how NLP is being used.

Applications of NLP

Because NLP is a subfield of artificial intelligence and computational linguistics that focuses on allowing computers to understand and interpret human language, it has a wide range of applications. Any use case that could benefit from machines able to read, interpret, and derive meaning from textual data, mimicking how humans communicate, is fair game. Consider these specific options:

• Automate tasks: Chatbots and AI agents that use NLP can process ever-more-complex tasks within an area of responsibility, such as invoicing, data analysis, or cybersecurity. The result is a new kind of efficiency. For example, an AI agent in an enterprise application could automatically extract relevant information from a vendor’s price quote, say a scanned PDF, then translate it if needed and create a purchase request within the system. This helps automate the procurement process and could also assist in automatically flagging the vendor’s final invoice for review by a manager, should the numbers differ.
• Improve search: Traditional NLP provides many techniques for improving keyword matching search and retrieval by recognizing words based on context. For example, “carrier” means something different in biomedical and logistics contexts. More recent architectures that leverage vector databases vastly improve NLP’s ability to understand the semantic meaning in human language. The vector embedding process assigns numerical representations to words, phrases, and entire documents, allowing tasks such as semantic search, sentiment analysis, and document similarity analysis to be performed quickly and with high accuracy. NLP-driven semantic searches are a key part of common services, such as the recommendation systems found on retail sites or entertainment streaming services.
• Analyze and organize large document collections: NLP techniques such as document clustering and topic modeling simplify the task of understanding the diversity of content in large document collections, such as corporate reports, news articles, and scientific documents. More recently, a growing number of embedding models have driven a new level of human language interaction with data and documents. Working within a retrieval-augmented generation (RAG) architecture, NLP-equipped applications can allow for the exploration of information in document stores using human language prompts rather than using SQL or other coding languages.
• Provide social media analytics: NLP can analyze customer reviews and social media comments to make better sense of huge volumes of information. Sentiment analysis identifies positive and negative comments in a stream of social media comments, providing a direct measure of customer sentiment in real time. Down the line, this can lead to huge payoffs, such as increased customer satisfaction and repeat business.
• Provide market insights: NLP can help analyze the language of a business’s customers, giving it a better handle on what they want and a better idea of how to communicate with them. For example, sentiment analysis can detect the specific aspects or products mentioned in social media (for example, “the keyboard is great, but the screen is too dim”), providing directly actionable information for product design and marketing.
• Moderate content: If your business has active social channels, NLP can help moderators track and react to what’s being said, giving them the opportunity to maintain quality and civility by analyzing not only the words, but also the tone and intent of comments. This can act as a backstop to common customer rating and flagging systems.

How NLP Works

NLP models most commonly use neural networks to learn patterns and representations from text training data. NLP models can be trained on large data sets to perform tasks like sentiment analysis, named entity recognition, machine translation, and text summarization. Within NLP, large language models learn to make predictions or generate text based on the patterns and features extracted from the input data.

The goal of NLP is to bridge the gap between human communication and computer understanding, enabling machines to perform tasks that require natural language comprehension. Here are some specific areas to consider.

• Computational Linguistics: Computational linguistics is a field of study that uses a combination of computer science, artificial intelligence, and linguistics to develop AI models that can process various parts of human language. The result is computational methods for analyzing and manipulating text and spoken language. Computational linguistics involves the study of syntax and grammar parsing, semantic analysis, and discourse analysis. The application of this study results in the NLP capabilities we see at work in machine translation, speech recognition, sentiment analysis, and language generation.
• Machine Learning in NLP: Because AI models learn to do various language-based tasks by analyzing the large training data sets that provide the basis for understanding language, modern NLP requires machine learning, or ML. The result in NLP is a machine learning model that accomplishes a target task, such as sentiment analysis, entity recognition, or language generation.
  
  For example, sentiment analysis training data consists of sentences labeled with their sentiment—for example, positive, negative, or neutral. A machine learning algorithm reads this data set and produces a model that takes sentences as input and returns their sentiments. The resulting document classification model can quickly tell if a document takes a positive, neutral, or negative view of the subject and whether it discusses, for example, sports, finance, or politics. Similarly, a machine learning model might be trained to recognize and classify entities within a document, such as names, places, and dates.
• Deep Learningin NLP: Deep learning is machine learning using deep neural network models. A deep neural network has multiple layers of interconnected nodes, or neurons, that allow the model to learn very complex patterns from its training data. Deep learning combined with large training data sets can improve performance on NLP tasks, such as machine translation, sentiment analysis, and speech recognition.
• Transfer Learning: Transfer learning, often referred to as AI model fine-tuning, involves taking a sophisticated foundation LLM and adapting it to a specific task using a smaller, task-specific data set. These foundation LLMs come with a strong grasp of language and a vast general knowledge that can be tuned to adjust to the nuances of a new task. In NLP, an organization can use transfer learning to help an AI model improve its accuracy in a local dialect, say, or to work within an industry with its own parlance, such as medical science.

NLP Implementation Steps

Common steps to implement NLP include:

1. Collect and prepare text data: Gather text data from various sources, such as social media, documents, or web content, and then preprocess it into a format suitable for analysis by machines.

2. Extract features and representation: Convert the preprocessed text into a numerical format that machine learning models can understand. The most advanced techniques involve converting word and text segments into vector embeddings.

3. Select and train a model: Choose an appropriate NLP model based on the task you want to perform, such as sentiment analysis or text classification, and then train it on your prepared data set, tweaking hyperparameters to optimize performance and accuracy.

4. Evaluate and deploy your model: Evaluate the NLP model for accuracy, precision, and recall and if it can generalize well when given new data. Once satisfied, deploy the model in a production environment to process and analyze text data in real-world settings.

Key NLP Techniques and Tasks

NLP uses AI to facilitate spoken interactions between machines and people. It involves a range of techniques and tasks to accomplish this.

• Preprocessing Techniques: In NLP, cleaning and preparing text data for analysis has traditionally been essential. These techniques include tokenization, which splits raw text—for example, a sentence or a document—into a sequence of tokens, such as words or subword pieces. Tokenization is often the first step in an NLP processing pipeline. Stemming and lemmatization then reduce words to their base or root form. For example, “revisited” consists of the prefix “re-,” the stem “visit,” and the past-tense suffix “-ed.” And stop word removal helps improve performance and save on processing by eliminating common words that don’t carry much meaning, typically short, frequent words such as “a,” “the,” and “an.”
  
  Additional preprocessing steps might include removing punctuation, handling special characters, and correcting spelling errors. These techniques help ensure that data is in a consistent and usable format for core NLP tasks.
• Core NLP Tasks: Core NLP tasks have evolved over time, but they all contribute to understanding the structure and meaning of text and are often used in combination to build the most complex NLP systems.
  
  Core NLP tasks break down human language so computers can recognize, extract, and imitate it. These tasks include part-of-speech (POS) tagging, which identifies the grammatical role of each word in a sentence—for example, noun, verb, or adjective—syntactic parsing to identify how words combine to form phrases, clauses, and entire sentences; named entity recognition, or NER, which identifies and classifies people, organizations, and locations; and sentiment analysis, which determines the emotional tone of a piece of text.
  
  More recently, deep neural networks have become state-of-the-art technology for LLMs, replacing both POS tagging and syntactic parsing with vector embeddings that provide more flexible and accurate manipulation of human language.
• Advanced NLP Tasks: LLMs rely on advanced NLP techniques to enable natural and engaging conversations between humans and machines. These methods can include automated translations from one language to another; text summarization, which provides more concise summaries of longer text passages; and question answering, which involves extracting and often paraphrasing information in a document to answer specific questions about the text. This natural language generation, or NLG, often requires sophisticated models, large data sets, and in many cases a fine-tuning process to take on tasks in specific domains, such as medicine or retail.

NLP in Various Industries

NLP can simplify and automate a wide range of business processes, especially ones that involve large amounts of unstructured text, such as emails, surveys, and social media conversations. With NLP, businesses can better analyze their data to help them make the right decisions. Here are just a few examples of practical applications of NLP.

• Healthcare: As healthcare systems across the world move to electronic health records, or EHRs, they accumulate huge amounts of unstructured data. NLP can analyze and gain new insights into health records while helping practitioners in fast-moving clinical settings add and update records, such as post-visit summaries, in the EHR without typing.
• Finance: In the financial field, traders use NLP technology to automatically mine information from corporate documents and news releases to extract information relevant to their portfolios and trading decisions.
• Customer service: Many companies use virtual assistants or chatbots to help answer basic customer inquiries and information requests, passing questions to humans only when necessary. More recently, LLMs connected to RAG architectures have become able to handle many of those more complex interactions.
• Insurance: Large insurance companies can use NLP to sift through documents and reports related to claims and very quickly deliver coverage information.

Challenges and Future of NLP

The NLP field has seen tremendous advancements, but it also faces challenges, as we’ll discuss. Every day, technology providers and researchers are working to make NLP systems more robust, adaptable, and capable of understanding and generating human-like language. Those efforts will yield significant advancements in areas including language translation, virtual assistants, and text analysis. Let’s look at some specific challenges and opportunities.

Current Challenges

Handling the complexity and ambiguity of human language, including understanding context, sarcasm, and nuances in different languages and dialects, is no small feat. NLP models often require vast amounts of labeled data for training, which can be time-consuming to create and expensive to acquire.

What other challenges are researchers tackling?

• Computational costs: As AI models increase in size and complexity, costs go up based on the number of computing cycles needed to accomplish tasks. Even with recent innovations in reinforcement learning, which can lower the time and cost of training regimens, NLP can still be expensive to run in production. ML engineers are exploring more efficient architectures and using methods such as model pruning and quantization in addition to reinforcement learning to lower computational costs.
• Data bias: Depending on the data sets used to train them, NLP models may be prone to generating text that’s skewed toward a particular group—simply mimicking the diction or dialect represented in the training data set. To overcome this, trainers must be aware if a particular demographic or context is overrepresented in the data set so they can augment it with more diverse language varieties. Fairness-aware algorithms can help you detect bias if you’re training your own LLMs.
• Interpretability: Interpretability in NLP is the ability to understand and explain the model’s outputs. This can be a challenge, especially with advanced LLMs where the internal workings are complex and, frankly, opaque. In settings where explaining the model’s reasoning is important, such as in legal, healthcare, and insurance decisions, interpretability is a must. As a result, there are a growing number of strategies to make model output more interpretable, including, most notably, reinforcement learning, as well as linear regression, decision trees, and a range of feature engineering techniques.

Future Trends

The future of NLP is focused on improving language understanding and generation while making the technology more accessible and beneficial for various applications. Researchers are working to develop more efficient algorithms, enhance multilingual capabilities, and create models that can learn with less labeled data.

NLP watchers can look to these trends:

• Advances in foundation models: Foundation models, such as Cohere, Llama, BERT, and GPT, continue to evolve, and the number of available models continues to grow. Some are becoming more sophisticated and versatile, while others focus on simplicity and targeted use, allowing them to be used for a wide range of tasks with minimal additional training and lower computational costs. Larger models are growing in versatility by integrating multimodal data, including text, images, video, and audio. Look for advances in architecture and infrastructure design, including recent developments in reinforcement learning, to accommodate complexity and scale while keeping costs in check.
• Improved understanding and generation: Although NLP has been in use for decades, it continues to make leaps in language understanding and generation, thanks to techniques that better capture the nuances of language, including context, sentiment, and intent. Look for NLP systems to continue to improve at tasks like machine translation, summarization, and natural language conversations with humans—especially as RAG architectures and knowledge graph technologies bring more contextually rich and accurate content to real world business applications.

Enhance Your NLP with Oracle GenAI

Did you know that Oracle Cloud Infrastructure (OCI) gives you everything you need to upgrade and improve even the most advanced NLP applications? OCI’s generative AI service, for example, offers simple integration with versatile LLMs—such as Cohere’s Command model or Meta’s open source Llama series—in an easy-to-use service. Use it to fine-tune models for a wide range of NLP use cases, including writing assistance, summarization, analysis, and chat.

For even easier access to the latest NLP for your business, Oracle SaaS applications offer instant access to AI outcomes wherever they’re needed—without leaving the software environment you use every day to power your business.

As NLP continues to evolve, it holds great potential to revolutionize how we interact with technology and process vast amounts of textual information.

Natural Language Processing (NLP) FAQs

How can NLP improve customer service?

NLP can help improve customer service in several ways. It can process a constant stream of spoken and written word queries from customers, allowing faster resolution of their issues. It does this by using sophisticated LLMs that understand the context and nuanced meaning in customer interactions. In the same way, it can also help human customer service agents better service customers by providing call summaries and “to dos” after a call.

What are the benefits of NLP in business analytics?

NLP opens insightful business analytics to a wider group of users. It does this by letting businesspeople explore data not through programming languages, such as SQL, but through natural language conversations with, for example, an AI agent that knows how to access, compile, and present data from the organization’s enterprise database.

How does NLP help automate business processes?

NLP helps automate business processes by understanding and generating language. For example, an NLP application might receive, invoice, and automatically initiate billing and fulfilment, requiring an employee to simply review and approve the activity. This can save time and effort with every invoice that’s processed.

How can NLP and AI together improve enterprise decision-making?

NLP depends on machine learning and often on sophisticated AI foundation models. All this AI power can help enterprise decision-making by bringing more flexibility and accessibility to data analytics. For example, an NLP-equipped analytics platform might offer an agentic interface that lets a businessperson ask questions of the organization’s enterprise database using natural language. This frees the businessperson from a preprogrammed dashboard and can lead to more creativity in data exploration.